Summary of Model-based Reward Shaping For Adversarial Inverse Reinforcement Learning in Stochastic Environments, by Simon Sinong Zhan et al.
Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments
by Simon Sinong Zhan, Qingyuan Wu, Philip Wang, Yixuan Wang, Ruochen Jiao, Chao Huang, Qi Zhu
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Model-Enhanced Adversarial Inverse Reinforcement Learning (AIRL) framework tackles the limitations of AIRL in stochastic environments by incorporating dynamics information into reward shaping. This ensures theoretical guarantees for optimal policy performance and outperforms existing baselines in sample efficiency. The novel approach integrates transition model estimation directly into reward shaping, providing a comprehensive analysis of reward error bounds and performance difference bounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers aim to improve Adversarial Inverse Reinforcement Learning (AIRL) in unpredictable environments where traditional methods don’t work well. They create a new way to combine the environment’s rules with rewards, making sure the results are reliable and efficient. The new method shows better performance in chaotic situations and is almost as good in predictable ones. |
Keywords
* Artificial intelligence * Reinforcement learning
